What if an MRI scan could determine whether a weight loss program was likely to be effective?
Ben-Gurion University of the Negev (BGU) researchers have discovered a neural subnetwork of connected regions between the brain and gastric basal electric frequency that correlates with future weight loss based on connectivity patterns.
BGU’s multidisciplinary team’s findings, published in the journal NeuroImage, support a prevalent neural theory that people with an increased neural response to seeing and smelling food consistently overeat and gain weight.
“To our surprise, we discovered that while higher executive functions, as measured behaviorally, were dominant factors in weight loss, this was not reflected in patterns of brain connectivity,” says Gidon Levakov, a graduate student, who led the study from the BGU Department of Brain and Cognitive Sciences.
“Consequently, we found that weight loss is not merely a matter of willpower, but is actually connected to much more basic visual and olfactory cues.”
The researchers identified a connection between the stomach basal electric rhythm within the subnetwork and weight loss.
That rhythm governs the gastric waves that are associated with hunger and satiety. They also found that the brain’s pericalcarine sulcus, the anatomical location of the primary visual cortex, was the most active node in this subnetwork.
The team assessed 92 people during an 18-month lifestyle weight loss intervention led by Prof. Iris Shai, of BGU’s Department of Epidemiology. The participants were selected based on large waist circumference, abnormal blood lipid levels and age.
Before the intervention, the participants underwent a battery of brain imaging scans and behavioral executive function tests. The participants’ weight loss was measured after six months of dieting, in which, according to Prof. Shai, the maximum weight loss is generally achieved.
The team found that the subnetwork of brain regions corresponded more closely to basic sensory and motor regions rather than higher, multi-modal regions.
“It appears that visual information may be an important factor triggering eating,” says principal investigator Prof. Galia Avidan, from the BGU Departments of Brain and Cognitive Sciences and Psychology.
“This is reasonable, given that vision is the primary sense in humans.”
The researchers note that these results may have significant implications toward understanding the cause of obesity and the mechanism of response to dieting.
Funding: This research was supported by grants from: The Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Projektnummer 209933838 – SFB 1052; the DFG, Obesity Mechanisms; Israel Ministry of Health (grant no. 87472511), Israel Ministry of Science and Technology (grant 3-13604), and the California Walnuts Commission.
Influence of obesity on brain structure and function
Cerebral hemodynamics
Accurate blood flow is crucial for survival and functioning of any organ, however the brain is fully dependent on blood flow for oxygen and glucose, and tissue damage may already occur after a very brief disruption in blood (Cipolla, 2009). Therefore, it is important to understand how obesity influences blood flow to and in the brain.
Studies show significant negative correlations between BMI and CBF velocity (CBFV) in the common and internal carotid arteries (Zhang et al., 2006). Lowered CBFV in obesity is associated with reduced cognitive performance independent of comorbid medical conditions. More importantly, the effect of BMI on CBFV seems to be independent of other factors such as hypertension and type 2 diabetes mellitus (Zhang et al., 2006; Selim et al., 2008).
Recently, it was found that obesity (measured as BMI and waist circumference) was negatively associated with resting GM CBF. This is an important finding, as GM CBF is generally correlated with cognitive functioning, implying that obesity may directly affect cognition via changes in CBF (Rusinek et al., 2015). Abdominal obesity is a major risk factor contributing to the metabolic syndrome (MetS). In a late middle-aged MetS group mean GM CBF was decreased compared to the control group (excluding medial and inferior parts of the occipital and temporal lobes). Interestingly, the MetS group also had lower immediate memory function (Birdsill et al., 2013).
Altogether, this implies that obesity may pose a risk for impaired blood flow to and in the brain. Contrarily, studies using positron-emission tomography have shown hypermetabolism in the brain in obesity, which might lead to an imbalance in reward systems and cognitive control (Iozzo et al., 2012).
Furthermore, CBF and oxygen metabolism in feeding-related brain regions is higher in obese individuals than in normal-weight persons (Karhunen et al., 1997). Possibly, increased activation in the right parietal cortex may relate to decreased feeding control, which could contribute to development and maintenance of the obese state (Karhunen et al., 1997).
Brain volume and integrity
It is well-established that obesity affects GM and WM integrity, probably caused by impaired CBF leading to ischemic stress and concomitant neuronal damage within the brain (Bobb et al., 2014).
Grey matter
Although there is increasing awareness that obesity is a risk factor for neurodegenerative diseases and cognitive decline, it is not yet clear how overweight relates to brain structural and functional changes. A large-scale population neuroimaging study showed a negative association between BMI (kg/m2), waist-to-hip ratio and fat index (total fat mass (kg)/height (m)) with overall GM volume (Hamer and Batty, 2019).
Another study reported that obese individuals showed decreased GM density in different brain areas, notably those involved in taste, reward and feeding/goal-directed behavior. Contrarily, greater GM density was also seen in obese subjects when compared to lean counterparts (Pannacciulli et al., 2006).
Subsequent studies on GM atrophy have examined volume and cortical thickness rather than density and found that obesity/BMI/waist circumference is as expected, inversely related to GM volume. However, some have focused more on pin-pointing the underlying cause of these GM changes, by distinguishing between different aspects of obesity.
For example, one study focused on the underlying cause of these changes, by distinguishing between fat mass and fat-free mass in overweight/obese individuals (Weise et al., 2013).
Interestingly, this study indicated that there is an association between excess fat/adiposity and GM atrophy, which is more attributed to the increased fat-free mass in obese individuals than increased body fat mass. Nevertheless, it was found that obesity is negatively related with GM volume, especially in the medial prefrontal cortex (mPFC) and the anterior cingulate cortex (ACC).
These structures are involved in decision making and inhibitory control (Weise et al., 2013). On the other hand, Janowitz et al. have linked waist circumference as a measure for abdominal obesity to GM volume changes, rather than simply BMI. With 2344 subjects, this large study has indicated that many brain regions are affected by abdominal obesity (Janowitz et al., 2015).
Rather than investigating GM density or volume, Shaw et al. focused on cortical thickness as a measure of GM integrity. According to this study, an inverse relationship between BMI and cortical thickness was found (Shaw et al., 2018). However more importantly, an association was discovered between cortical thinning and increased visceral WAT, when adjusted for BMI score (Veit et al., 2014b).
This relates well to the study by Janowitz et al. on effects of abdominal obesity, which indicates that these GM changes are possibly caused by inflammatory responses due to adipokine release by central WAT (Janowitz et al., 2015). Similar to other studies, Veit et al. showed an association between increased BMI and increased visceral WAT with reduced GM thickness in several brain areas (Veit et al., 2014a).
In conclusion, there is increasing evidence for obesity measures being associated with GM volumes, although the exact associations and mechanisms are still under debate. Furthermore, many studies are performed cross-sectional, and therefore it is not possible to definitely state the direction of the associations.
White matter
It has become clear that obesity not only influences integrity of GM in the brain but WM and structural connectivity is affected by adiposity as well (Verstynen et al., 2013). Conclusions from adiposity and WM integrity association studies resemble those about GM integrity. Indeed, several studies using diffusion weighted imaging have shown negative correlations between obesity measures and fiber connectivity (Kullmann et al., 2016; Verstynen et al., 2013; Xu et al., 2013; Bolzenius et al., 2015; van Bloemendaal et al., 2016; Alarcon et al., 2016).
Interestingly, not all studies have implicated the same regions, although there is some overlap in results. Affected WM structures comprise for example the corpus callosum (genu, trunk and splenium), cerebellar peduncle, corona radiata (Verstynen et al., 2013; Xu et al., 2013), fornix (Xu et al., 2013), and the uncinate fasciculus in older adults (Bolzenius et al., 2015).
One of these studies has indicated a decrease in WM volume using voxel based morphometry analysis (van Bloemendaal et al., 2016). Kullmann et al. revealed regionally specific changes in mean diffusivity and a strong decrease in axial diffusivity in obese young adults in the corticospinal tract, anterior thalamic radiation and superior longitudinal fasciculus indicating an increased risk for cognitive decline in obese individuals (Kullmann et al., 2016).
It is important to note that Hamer et al. did not find any association between obesity measures and WM and others even observed a positive interaction between BMI and WM integrity and volume (Hamer and Batty, 2019; Koivukangas et al., 2016; Haltia et al., 2007).
In short, there is less conclusive evidence about the associations between obesity measures and WM compared to GM. An increase in WM volume might be due to an abnormal lipid metabolism and therefore fat accumulation in myelin throughout the brain (Haltia et al., 2007). More importantly, it would be interesting to see whether these WM changes affect cognitive impairment.
Influence of obesity on cognitive functioning
Food-related stimulus processing
Areas concerning feeding behavior, such as the frontal operculum, post-central gyrus, dorsal striatum, prefrontal cortex and hippocampus, often show a decreased volume in obesity (Pannacciulli et al., 2006; Janowitz et al., 2015). Concurrent with this, obese people do in fact show different responses to visual food cues. When observing high-calorie foods obese women show higher blood oxygen level dependent (BOLD) activation in the dorsal striatum, a brain area that has been implicated in habit learning and addictive behavior (Rothemund et al., 2007).
Moreover, obese children, adolescents and adults show higher activation of several brain regions, including the nucleus accumbens and caudate nucleus, compared to normal-weight controls when tasting sweet, bitter and high-calorie substances (Feldstein Ewing et al., 2017; Boutelle et al., 2015; Szalay et al., 2012).
In general, it is noteworthy that increased BMI/waist circumference is associated with altered gustatory perception, although it should be investigated whether this is a cause or consequence of obesity.
In addition, obesity has been shown to be associated with aberrant reward responsivity. Several studies have indicated that connectivity in reward-related networks is less strong in obese individuals in comparison to normal-weight counterparts (Garcia-Garcia et al., 2013; Wijngaarden et al., 2015).
However, there appears to be a stronger activation of reward-processing areas during tasks such as monetary reward paradigms (Opel et al., 2015). This suggests that disinhibition takes place due to decreased connectivity. Additionally, BMI is positively associated with serotonin availability in areas such as the nucleus accumbens and ventral pallidum, which are involved in reward processing (Haahr et al., 2012).
This indicates that obese individuals have a stronger sense of reward after ingestion of palatable foods. Increased serotonin levels have also been found in hippocampus and the orbitofrontal cortex in obese subjects, which are both involved in (food) reward learning and processing (Haahr et al., 2012).
Moreover, obesity is associated with changes in activity of brain regions that are related to feeding behavior and stronger reward activity (Rothemund et al., 2007; Szalay et al., 2012). This suggests that these alterations cause obesity, rather than obesity causes changes in brain activity (Janowitz et al., 2015).
Cognitive function and control
Obesity has been associated with decreased memory performance and learning ability, as shown through various parameters. For example, it has been found that working memory is decreased in obese individuals when compared to normal-weight counterparts (Stingl et al., 2012).
Interestingly, this was associated with an increase in neural activity, rather than a decrease, during the early phase after stimulus presentation. This possibly indicates disinhibition, which has indeed been observed in obesity and can lead to insufficient suppression of unwanted responses, thereby decreasing accuracy and reaction speed (Stingl et al., 2012).
Additionally, recent evidence suggests that obese individuals exhibit inadequate implicit learning, for example by failing to apply negative prediction error in tasks requiring adaptation of behavior. This is possibly due to inadequate dopamine signaling (Mathar et al., 2017).
It has further been shown that compared to normal-weight individuals, obese participants exhibit decreased activity in regions associated to memory and learning, such as the hippocampus, angular gyrus, precuneus and the parahippocampal gyrus and parts of the prefrontal cortex.
Areas such as these have been implicated to be affected by obesity, making this decreased activity consistent with findings of volume and density loss mentioned earlier (Cheke et al., 2017).
Lastly, there are reports on increased impulsivity/lack of inhibitory control in obese individuals, which is in accordance with structural alterations observed in regions associated with cognitive control (Weise et al., 2013; Skoranski et al., 2013).
However, it is difficult to assess whether obesity causes increased impulsivity or vice versa, as it seems more plausible that impulsive individuals have a higher disposition to develop obesity. Nevertheless, this association should be investigated further.
Underlying mechanisms between obesity and brain structure and function
As mentioned earlier, it has been suggested that especially visceral and abdominal WAT becomes inflamed and dysregulated in obesity, producing adipokines, such as inflammatory cytokines that can cause inflammation (Verstynen et al., 2013). Examples are monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor-α (TNF-α) and interleukins (IL) such as IL-6 and IL-β (Kiliaan et al., 2014; Jaganathan et al., 2018).
This increased secretion of inflammatory factors has been associated with damage to food-intake regulating circuits of the brain (Cazettes et al., 2011). Moreover, the pro-inflammatory IL-6 may especially affects hippocampal volume and function (Kiliaan et al., 2014).
These and other adipokines, such as angiotensinogen, serum amyloid A (SAA) and plasminogen activator inhibitor-1 (PAI-1) have adverse effects on the cardiovascular system, such as hypertension, thrombosis, atherosclerosis and endothelial dysfunction which may contribute to the changes observed in the blood circulation and consequently CBF (Arnoldussen et al., 2014; Kiliaan et al., 2014; Verstynen et al., 2013). Additionally, altered CBF itself has a negative effect on cognitive function (Rusinek et al., 2015).
Leptin is one of the more well-known adipokines that may contribute to the negative effects of obesity on the brain. Leptin can have various roles in the brain such as energy intake regulation in the hypothalamus, memory, neurogenesis and brain structure (Arnoldussen et al., 2014). It has been shown that concentrations of leptin in the blood are negatively correlated with GM volume (Pannacciulli et al., 2007).
Changes in WM in obesity seem to be related to vascular and inflammatory factors as well (Bettcher et al., 2013). For example, inflammatory cytokines can lead to a cellular response of microglia leading to more water in brain tissue causing loss of WM integrity (Rosano et al., 2012; Kullmann et al., 2015).
Sex differences should be considered when looking at WAT in the relation between the obese phenotype and brain function and structure (Horstmann et al., 2011). There are significant discrepancies in fat distribution between men and women which might lead to different effects on the brain.
Most importantly, men typically store more fat in the abdomen, whereas in women, fat is mostly stored in gluteofemoral WAT (White and Tchoukalova, 2014). This is also associated with sex-related differences in adipokine levels (Kiliaan et al., 2014). Therefore, it is important to have equal representation of males and females in studies.
Next to WAT, the gut, gut hormones and its microbiome have large effects on the brain, such as regulating eating behavior (Torres-Fuentes et al., 2017). It has been found that microbiota of obese individuals are different and less diverse compared to lean individuals, with a lower proportion of the bacteria group Bacteroidetes and higher proportion of Firmicutes (Ley et al., 2006). Additionally, gut microbiota can generate short chain fatty acids (SCFAs) via fermentation of dietary fibers.
In obesity, increased SCFA concentrations are observed and these SCFAs can influence the production of neurotransmitters and their precursors (van de Wouw et al., 2017; Schwiertz et al., 2010). Furthermore, gut microbiota can affect gastrointestinal barrier permeability. As shown in diet-induced obese mice, obesity is associated with increased gut permeability (Lam et al., 2012).
Therefore, it is plausible that the obese phenotype is related to changes in the gut as well as changes in WAT, which influence brain function and structure (see Fig. 1a).
